Well acknowledged nationally and internationally, the problem of early diagnosis of dementia, particularly Alzheimer's disease and Mild cognitive deficit, is a landmark problem in chronic diseases and public health worldwide. This burden is particularly becoming a demographic problem, not only in industrial nations, but also in developing countries like China, India and Brazil (where population above 50 year are increasing dramatically). Numerous studies by W.H.O, N.I.H, European Commission and World Bank have underscored the extreme need for an automated ready objective imaging test for diagnosing dementia, which can also be used by a technician (non-physician) in an epidemiological or community based screening perspective to monitor geriatric population with memory problem. The proposed invention exactly fits all these requirements. This invention will be of much interest and utility to the medical, neurological, psychiatric, radiological, psychotherapeutic and geriatric community, as well as to imaging scientists and engineers, epidemiologists, public health specialists or policy-makers, who need to tackle or plan for the ever-growing societal burden imposed by the rapidly increasing elderly population.
Most of the earlier pathological/biochemical techniques for Alzheimer diagnosis, use biopolymer markers, peptide markers, or markers for amyloid deposits and tau proteins. These procedures are invasive, and need brain tissue materials from the patient, and are, hence, not commonly feasible. On the other hand, there are morphometric or volumetric imaging techniques to correlate with dementia, but there procedures need long time-consuming manual intervention by neuroradiologists (not usually obtainable), and suffer from human visual subjective errors, the accuracy being around 75-90%. Furthermore, other exploratory neuroimaging procedures that use automated image processing approach to diagnose Alzheimer's disease, have an accuracy up to 85-89%, and rely on imaging every voxel of brain, with heavy computational processing (using a space of about 100,000 dimensions), whilst needing repeated manual checking of misregistration and image thresholding. All these said techniques have utilized patient data sets below 100 individuals, whereas the development of inventor's technique has involved much more individuals, around 200 subjects.
The quantitative procedure and computational algorithms for the classification of the neurodegenerative brain image based on T1-weighted MR scan has been developed and the procedure has been validated using tested clinical patient datasets (over a large population of over 200 individuals), and the procedure is applicable to MRI scanners of all the Electronics Engineering Manufacturers in the world who make these equipments, such as Siemens (Germany), General Electric (USA), Philips (The Netherlands), Picker (UK), LG Electronics (Korea), Toshiba (Japan) etc. Our testing has been done on two different datasets of dementia and Alzheimer's disease at different centers and of scanners of different manufactures. At first, we initially explored the feasibility of the initial image standardization methodology by using image from various medical centers in India, across the four zones of the country, namely North (Delhi), South (Bangalore), East (Calcutta) and West (Bombay).
Thereafter, inventors evolved the technology by using and testing the various clinical imaging scan datasets taken under standard protocols [such as OASIS platform of National Institute of Health (NIH), and LONI platform of Alzheimer's Disease Neuroimaging Initiative (ADNI). The validation of the proposed approach has been done on randomly selected ⅔ of patients as training sets and ⅓ of patients as unseen testing sets. This was repeated three times, by further randomized selection. For each training set, we built a classification algorithm based on specific topological indices, thence we imparted a performance trial of the algorithm by using them on the unseen test set. Inventors found that the same algorithm performed satisfactory classification on all the instances. Our technique is user friendly and automated, does not need any physician or doctor to intervene, and can also be used by a wide range of community as an objective screening methodology. The invented image processing procedure has been coded using MatLab language, and can be extended to open source freeware.
Most of the earlier pathological/biochemical techniques to address the problem of dementia diagnosis, actually use biopolymer markers, protein markers, amyloid deposits and peptides. These procedures are invasive, and need cellular materials from the patient, and are, hence, not commonly feasible. Therefore the latter methodology, being invasive, does not satisfy our requirement of being a harmless non-invasive technique. On the other hand, in the radiological diagnosis field, there are morphometric or volumetric imaging techniques to correlate with dementia, but these procedures need long time-consuming manual intervention by neuro-radiologists (who are much cost-intensive and not usually readily obtainable), and these procedures suffer from human visual subjective errors, the accuracy being around 75-90%. Furthermore, other exploratory neuroimaging procedures that use automated image processing approach to diagnose Alzheimer's disease, have an accuracy up to only 85-89% (compared to our 99%), and rely on imaging every voxel of brain, with heavy computational processing (in parametric space of about 100,000 dimensional feature vectors, in comparison to our parametric space of 2 or 3 dimensional feature vectors). Further those exploratory neuroimaging procedures need repeated manual checking in misregistration and image thresholding. In other words, our technique is considerable superior to the existing state of art.
There are a number of drawbacks and limitations in the existing imaging techniques. Their accuracy is lower, and they are neither rapid, automatic nor technician-operable. It is not possible to get all these requirements in a single method already existing commercially. It has been mentioned that for classifying dementia from plain raw MR images, there is no published patent that corresponds to our technique that implements the requirements of being brisk, automatic, objective, and over accurate (99%), without being computationally intensive nor requiring manual processing or intervention from radiologists. There are patents, which can satisfy parts of these requirements, but not the whole.
For instance, some procedures involve time-consuming morphometry by radiologists, while others are visually subjective or need point-by-point plotting of cortical deformation/thickness with heavy computer processing being involved (in a space of about 100,000 dimensions); indeed some of the processes known uses repeated manual checking in misregistration and image thresholding. These existing procedures have accuracy between 76-89%. All these techniques have utilized patient data sets below 100 individuals, whereas the development of our technique has involved much more individuals, around 200 subjects. Thus there is no patent satisfying all the wide-ranging requirements satisfied by the proposed invention.
To get a fundamental index of the cognitive dementic brain, inventors have symbiotically devised the method based on two powerful concepts:                (i) The Biological concept of the “Ventricualr zone”, from which the cortex develops and which is the only region in the adult human brain that produces distant cortical neurogenesis.        (ii) The Mathematical concept of the “Topological dimensionality” which is a compact rigorous characterization of any space or contour that has a natural grainy irregularity in its disposition.        
Inventors have selected the ventricular zone because different dementic diseases has different signatures on this ventricular zone which can be contoured in a single-projection single-slice MRI scan. Thereby, the topological dimensionality of the ventricular zone contour would be a very economical (and hence computationally readily measurable) index that would be a characteristic signature of the dementic process.
From a neurobiological viewpoint, the topological pattern of the ventricular zone (the original neural germinal tissue) is actually the generative template behind the development cerebral cortex and its distortion. Inventors topological dimension-computing algorithm is simple to operate and they have automated it. They have tested the feasibility of normalizability of the image processing operation by taking care to use image samples from all the regions of India with different ethnic groups: Northern, Southern, Eastern and Western zones. Inventors classification procedure in motivated by the principle of machine learning algorithm and artificial intelligence, which have earlier shown considerable ability to distinguish and classify biological signals in other contexts. Furthermore, inventors have taken much care and efforts to develop the technique using imaging inputs from scanners of different companies, and utilizing a large sample of over 200 patients, so that errors become much less and the statistical power of the analysis becomes much high.
However, among the methods available, it is known that imaging methods give more accuracy in dementia classification that methods using biochemical biomarker tests of body materials. There are a few patents on dementia classification using imaging analysis or biomarker analysis, such as Rubenstein et al., 1999 (U.S. Pat. No. 6,264,625), Rosse, et al, 1999 (U.S. Pat. No. 5,956,125), Shimura, et al., 2003 (U.S. Pat. No. 6,654,695), Scinto and Daffner, 2000 (U.S. Pat. No. 6,024,709), Kluger et al. (U.S. Pat. No. 6,067,986), Jackowski and Marshall (U.S. Pat. No. 7,074,576), Takahashi et al (U.S. Pat. No. 7,070,945). Nevertheless, these patents do not have as much accuracy as our proposed technique; moreover the existing procedures do not deal with any ready automated technique of image classification of dementic patients (which is the topic of the invention).